Automated detection of shared-risk link groups in optical networks using fiber sensing

Abstract

Distributed acoustic sensing (DAS) has generated significant excitement across diverse industries and applications under the common theme of using deployed fiber to identify events in the environment based on acoustic modulation of backscattered light. In this paper, we focus on an application of critical importance to telecommunications networks: identifying shared-risk link groups (SRLGs). The underlying principle for SRLG detection is the presence of correlated acoustic signatures in fibers that share common physical paths. We present an algorithm based on constructing spatial correlation maps and detecting the line-segment patterns characteristic of shared links. We demonstrate successful application of the algorithm to a variety of fiber topologies. As currently no automated mechanism exists for discovering SRLGs at the passive fiber level, DAS-based detection has the potential of transforming how fiber assets are managed to design ever more resilient networks.